Field operations neural network heuristics

ABSTRACT

A method includes representing oilfield operational plan information as pixels where the pixels include pixels that correspond to a plurality of different state variables associated with oilfield operations; training a deep neural network based at least in part on the pixels to generate a trained deep neural network; implementing the trained deep neural network during generation of an oilfield operational plan; and outputting the oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.

RELATED APPLICATIONS

This application claims priority to and the benefit of a U.S.Provisional Application having Ser. No. 62/431,853, filed 9 Dec. 2016,which is incorporated by reference herein.

BACKGROUND

A resource field can be an accumulation, pool or group of pools of oneor more resources (e.g., oil, gas, oil and gas) in a subsurfaceenvironment. A resource field can include at least one reservoir. Areservoir may be shaped in a manner that can trap hydrocarbons and maybe covered by an impermeable or sealing rock. A bore can be drilled intoan environment where the bore may be utilized to form a well that can beutilized in producing hydrocarbons from a reservoir.

A rig can be a system of components that can be operated to form a borein an environment, to transport equipment into and out of a bore in anenvironment, etc. As an example, a rig can include a system that can beused to drill a bore and to acquire information about an environment,about drilling, etc. A resource field may be an onshore field, anoffshore field or an on- and offshore field. A rig can includecomponents for performing operations onshore and/or offshore. A rig maybe, for example, vessel-based, offshore platform-based, onshore, etc.

Field planning can occur over one or more phases, which can include anexploration phase that aims to identify and assess an environment (e.g.,a prospect, a play, etc.), which may include drilling of one or morebores (e.g., one or more exploratory wells, etc.). Other phases caninclude appraisal, development and production phases.

SUMMARY

A method can include representing oilfield operational plan informationas pixels where the pixels include pixels that correspond to a pluralityof different state variables associated with oilfield operations;training a deep neural network based at least in part on the pixels togenerate a trained deep neural network; implementing the trained deepneural network during generation of an oilfield operational plan; andoutputting the oilfield operational plan as a digital plan thatspecifies at least one control action for oilfield equipment.

A system can include a processor; memory accessible by the processor;processor-executable instructions stored in the memory and executable toinstruct the system to: represent oilfield plan information as pixelswhere the pixels include pixels that correspond to a plurality ofdifferent state variables associated with oilfield operations; train adeep neural network based at least in part on the pixels to generate atrained deep neural network; and implement the trained deep neuralnetwork during generation of an oilfield operational plan that specifiesat least one control action for oilfield equipment.

One or more computer-readable storage media can includeprocessor-executable instructions to instruct a computing system to:represent oilfield plan information as pixels where the pixels includepixels that correspond to a plurality of different state variablesassociated with oilfield operations; train a deep neural network basedat least in part on the pixels to generate a trained deep neuralnetwork; and implement the trained deep neural network during generationof an oilfield operational plan that specifies at least one controlaction for oilfield equipment. Various other apparatuses, systems,methods, etc., are also disclosed.

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be morereadily understood by reference to the following description taken inconjunction with the accompanying drawings.

FIG. 1 illustrates examples of equipment in a geologic environment;

FIG. 2 illustrates examples of equipment and examples of hole types;

FIG. 3 illustrates an example of a system;

FIG. 4 illustrates an example of a system;

FIG. 5 illustrates an example of a graphical user interface;

FIG. 6 illustrates an example of a graphical user interface;

FIG. 7 illustrates an example of a system;

FIG. 8 illustrates an example of a system;

FIG. 9 illustrates an example of a system;

FIG. 10 illustrates an example of a system;

FIG. 11 illustrates an example of a policy network image stack and avalue network image stack;

FIG. 12 illustrates an example of an image representation of planinformation;

FIG. 13 illustrates an example of an image representation of planinformation;

FIG. 14 illustrates an example of a method and an example of a system;

FIG. 15 illustrates an example of computing system; and

FIG. 16 illustrates example components of a system and a networkedsystem.

DETAILED DESCRIPTION

The following description includes the best mode presently contemplatedfor practicing the described implementations. This description is not tobe taken in a limiting sense, but rather is made merely for the purposeof describing the general principles of the implementations. The scopeof the described implementations should be ascertained with reference tothe issued claims.

FIG. 1 shows an example of a geologic environment 120. In FIG. 1 , thegeologic environment 120 may be a sedimentary basin that includes layers(e.g., stratification) that include a reservoir 121 and that may be, forexample, intersected by a fault 123 (e.g., or faults). As an example,the geologic environment 120 may be outfitted with any of a variety ofsensors, detectors, actuators, etc. For example, equipment 122 mayinclude communication circuitry to receive and to transmit informationwith respect to one or more networks 125. Such information may includeinformation associated with downhole equipment 124, which may beequipment to acquire information, to assist with resource recovery, etc.Other equipment 126 may be located remote from a well site and includesensing, detecting, emitting or other circuitry. Such equipment mayinclude storage and communication circuitry to store and to communicatedata, instructions, etc. As an example, one or more pieces of equipmentmay provide for measurement, collection, communication, storage,analysis, etc. of data (e.g., for one or more produced resources, etc.).As an example, one or more satellites may be provided for purposes ofcommunications, data acquisition, etc. For example, FIG. 1 shows asatellite in communication with the network 125 that may be configuredfor communications, noting that the satellite may additionally oralternatively include circuitry for imagery (e.g., spatial, spectral,temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 120 as optionally includingequipment 127 and 128 associated with a well that includes asubstantially horizontal portion that may intersect with one or morefractures 129. For example, consider a well in a shale formation thatmay include natural fractures, artificial fractures (e.g., hydraulicfractures) or a combination of natural and artificial fractures. As anexample, a well may be drilled for a reservoir that is laterallyextensive. In such an example, lateral variations in properties,stresses, etc. may exist where an assessment of such variations mayassist with planning, operations, etc. to develop the reservoir (e.g.,via fracturing, injecting, extracting, etc.). As an example, theequipment 127 and/or 128 may include components, a system, systems, etc.for fracturing, seismic sensing, analysis of seismic data, assessment ofone or more fractures, injection, production, etc. As an example, theequipment 127 and/or 128 may provide for measurement, collection,communication, storage, analysis, etc. of data such as, for example,production data (e.g., for one or more produced resources). As anexample, one or more satellites may be provided for purposes ofcommunications, data acquisition, etc.

FIG. 1 also shows an example of equipment 170 and an example ofequipment 180. Such equipment, which may be systems of components, maybe suitable for use in the geologic environment 120. While the equipment170 and 180 are illustrated as land-based, various components may besuitable for use in an offshore system.

The equipment 170 includes a platform 171, a derrick 172, a crown block173, a line 174, a traveling block assembly 175, drawworks 176 and alanding 177 (e.g., a monkeyboard). As an example, the line 174 may becontrolled at least in part via the drawworks 176 such that thetraveling block assembly 175 travels in a vertical direction withrespect to the platform 171. For example, by drawing the line 174 in,the drawworks 176 may cause the line 174 to run through the crown block173 and lift the traveling block assembly 175 skyward away from theplatform 171; whereas, by allowing the line 174 out, the drawworks 176may cause the line 174 to run through the crown block 173 and lower thetraveling block assembly 175 toward the platform 171. Where thetraveling block assembly 175 carries pipe (e.g., casing, etc.), trackingof movement of the traveling block 175 may provide an indication as tohow much pipe has been deployed.

A derrick can be a structure used to support a crown block and atraveling block operatively coupled to the crown block at least in partvia line. A derrick may be pyramidal in shape and offer a suitablestrength-to-weight ratio. A derrick may be movable as a unit or in apiece by piece manner (e.g., to be assembled and disassembled).

As an example, drawworks may include a spool, brakes, a power source andassorted auxiliary devices. Drawworks may controllably reel out and reelin line. Line may be reeled over a crown block and coupled to atraveling block to gain mechanical advantage in a “block and tackle” or“pulley” fashion. Reeling out and in of line can cause a traveling block(e.g., and whatever may be hanging underneath it), to be lowered into orraised out of a bore. Reeling out of line may be powered by gravity andreeling in by a motor, an engine, etc. (e.g., an electric motor, adiesel engine, etc.).

As an example, a crown block can include a set of pulleys (e.g.,sheaves) that can be located at or near a top of a derrick or a mast,over which line is threaded. A traveling block can include a set ofsheaves that can be moved up and down in a derrick or a mast via linethreaded in the set of sheaves of the traveling block and in the set ofsheaves of a crown block. A crown block, a traveling block and a linecan form a pulley system of a derrick or a mast, which may enablehandling of heavy loads (e.g., drillstring, pipe, casing, liners, etc.)to be lifted out of or lowered into a bore. As an example, line may beabout a centimeter to about five centimeters in diameter as, forexample, steel cable. Through use of a set of sheaves, such line maycarry loads heavier than the line could support as a single strand.

As an example, a derrickman may be a rig crew member that works on aplatform attached to a derrick or a mast. A derrick can include alanding on which a derrickman may stand. As an example, such a landingmay be about 10 meters or more above a rig floor. In an operationreferred to as trip out of the hole (TOH), a derrickman may wear asafety harness that enables leaning out from the work landing (e.g.,monkeyboard) to reach pipe in located at or near the center of a derrickor a mast and to throw a line around the pipe and pull it back into itsstorage location (e.g., fingerboards), for example, until it a time atwhich it may be desirable to run the pipe back into the bore. As anexample, a rig may include automated pipe-handling equipment such thatthe derrickman controls the machinery rather than physically handlingthe pipe.

As an example, a trip may refer to the act of pulling equipment from abore and/or placing equipment in a bore. As an example, equipment mayinclude a drillstring that can be pulled out of a hole and/or placed orreplaced in a hole. As an example, a pipe trip may be performed where adrill bit has dulled or has otherwise ceased to drill efficiently and isto be replaced.

FIG. 2 shows an example of a wellsite system 200 (e.g., at a wellsitethat may be onshore or offshore). As shown, the wellsite system 200 caninclude a mud tank 201 for holding mud and other material (e.g., wheremud can be a drilling fluid), a suction line 203 that serves as an inletto a mud pump 204 for pumping mud from the mud tank 201 such that mudflows to a vibrating hose 206, a drawworks 207 for winching drill lineor drill lines 212, a standpipe 208 that receives mud from the vibratinghose 206, a kelly hose 209 that receives mud from the standpipe 208, agooseneck or goosenecks 210, a traveling block 211, a crown block 213for carrying the traveling block 211 via the drill line or drill lines212 (see, e.g., the crown block 173 of FIG. 1 ), a derrick 214 (see,e.g., the derrick 172 of FIG. 1 ), a kelly 218 or a top drive 240, akelly drive bushing 219, a rotary table 220, a drill floor 221, a bellnipple 222, one or more blowout preventors (BOPs) 223, a drillstring225, a drill bit 226, a casing head 227 and a flow pipe 228 that carriesmud and other material to, for example, the mud tank 201.

In the example system of FIG. 2 , a borehole 232 is formed in subsurfaceformations 230 by rotary drilling; noting that various exampleembodiments may also use directional drilling.

As shown in the example of FIG. 2 , the drillstring 225 is suspendedwithin the borehole 232 and has a drillstring assembly 250 that includesthe drill bit 226 at its lower end. As an example, the drillstringassembly 250 may be a bottom hole assembly (BHA).

The wellsite system 200 can provide for operation of the drillstring 225and other operations. As shown, the wellsite system 200 includes theplatform 211 and the derrick 214 positioned over the borehole 232. Asmentioned, the wellsite system 200 can include the rotary table 220where the drillstring 225 pass through an opening in the rotary table220.

As shown in the example of FIG. 2 , the wellsite system 200 can includethe kelly 218 and associated components, etc., or a top drive 240 andassociated components. As to a kelly example, the kelly 218 may be asquare or hexagonal metal/alloy bar with a hole drilled therein thatserves as a mud flow path. The kelly 218 can be used to transmit rotarymotion from the rotary table 220 via the kelly drive bushing 219 to thedrillstring 225, while allowing the drillstring 225 to be lowered orraised during rotation. The kelly 218 can pass through the kelly drivebushing 219, which can be driven by the rotary table 220. As an example,the rotary table 220 can include a master bushing that operativelycouples to the kelly drive bushing 219 such that rotation of the rotarytable 220 can turn the kelly drive bushing 219 and hence the kelly 218.The kelly drive bushing 219 can include an inside profile matching anoutside profile (e.g., square, hexagonal, etc.) of the kelly 218;however, with slightly larger dimensions so that the kelly 218 canfreely move up and down inside the kelly drive bushing 219.

As to a top drive example, the top drive 240 can provide functionsperformed by a kelly and a rotary table. The top drive 240 can turn thedrillstring 225. As an example, the top drive 240 can include one ormore motors (e.g., electric and/or hydraulic) connected with appropriategearing to a short section of pipe called a quill, that in turn may bescrewed into a saver sub or the drillstring 225 itself. The top drive240 can be suspended from the traveling block 211, so the rotarymechanism is free to travel up and down the derrick 214. As an example,a top drive 240 may allow for drilling to be performed with more jointstands than a kelly/rotary table approach.

In the example of FIG. 2 , the mud tank 201 can hold mud, which can beone or more types of drilling fluids. As an example, a wellbore may bedrilled to produce fluid, inject fluid or both (e.g., hydrocarbons,minerals, water, etc.).

In the example of FIG. 2 , the drillstring 225 (e.g., including one ormore downhole tools) may be composed of a series of pipes threadablyconnected together to form a long tube with the drill bit 226 at thelower end thereof. As the drillstring 225 is advanced into a wellborefor drilling, at some point in time prior to or coincident withdrilling, the mud may be pumped by the pump 204 from the mud tank 201(e.g., or other source) via a the lines 206, 208 and 209 to a port ofthe kelly 218 or, for example, to a port of the top drive 240. The mudcan then flow via a passage (e.g., or passages) in the drillstring 225and out of ports located on the drill bit 226 (see, e.g., a directionalarrow). As the mud exits the drillstring 225 via ports in the drill bit226, it can then circulate upwardly through an annular region between anouter surface(s) of the drillstring 225 and surrounding wall(s) (e.g.,open borehole, casing, etc.), as indicated by directional arrows. Insuch a manner, the mud lubricates the drill bit 226 and carries heatenergy (e.g., frictional or other energy) and formation cuttings to thesurface where the mud (e.g., and cuttings) may be returned to the mudtank 201, for example, for recirculation (e.g., with processing toremove cuttings, etc.).

The mud pumped by the pump 204 into the drillstring 225 may, afterexiting the drillstring 225, form a mudcake that lines the wellborewhich, among other functions, may reduce friction between thedrillstring 225 and surrounding wall(s) (e.g., borehole, casing, etc.).A reduction in friction may facilitate advancing or retracting thedrillstring 225. During a drilling operation, the entire drill string225 may be pulled from a wellbore and optionally replaced, for example,with a new or sharpened drill bit, a smaller diameter drill string, etc.As mentioned, the act of pulling a drill string out of a hole orreplacing it in a hole is referred to as tripping. A trip may bereferred to as an upward trip or an outward trip or as a downward tripor an inward trip depending on trip direction.

As an example, consider a downward trip where upon arrival of the drillbit 226 of the drill string 225 at a bottom of a wellbore, pumping ofthe mud commences to lubricate the drill bit 226 for purposes ofdrilling to enlarge the wellbore. As mentioned, the mud can be pumped bythe pump 204 into a passage of the drillstring 225 and, upon filling ofthe passage, the mud may be used as a transmission medium to transmitenergy, for example, energy that may encode information as in mud-pulsetelemetry.

As an example, mud-pulse telemetry equipment may include a downholedevice configured to effect changes in pressure in the mud to create anacoustic wave or waves upon which information may modulated. In such anexample, information from downhole equipment (e.g., one or more modulesof the drillstring 225) may be transmitted uphole to an uphole device,which may relay such information to other equipment for processing,control, etc.

As an example, telemetry equipment may operate via transmission ofenergy via the drillstring 225 itself. For example, consider a signalgenerator that imparts coded energy signals to the drillstring 225 andrepeaters that may receive such energy and repeat it to further transmitthe coded energy signals (e.g., information, etc.).

As an example, the drillstring 225 may be fitted with telemetryequipment 252 that includes a rotatable drive shaft, a turbine impellermechanically coupled to the drive shaft such that the mud can cause theturbine impeller to rotate, a modulator rotor mechanically coupled tothe drive shaft such that rotation of the turbine impeller causes saidmodulator rotor to rotate, a modulator stator mounted adjacent to orproximate to the modulator rotor such that rotation of the modulatorrotor relative to the modulator stator creates pressure pulses in themud, and a controllable brake for selectively braking rotation of themodulator rotor to modulate pressure pulses. In such example, analternator may be coupled to the aforementioned drive shaft where thealternator includes at least one stator winding electrically coupled toa control circuit to selectively short the at least one stator windingto electromagnetically brake the alternator and thereby selectivelybrake rotation of the modulator rotor to modulate the pressure pulses inthe mud.

In the example of FIG. 2 , an uphole control and/or data acquisitionsystem 262 may include circuitry to sense pressure pulses generated bytelemetry equipment 252 and, for example, communicate sensed pressurepulses or information derived therefrom for process, control, etc.

The assembly 250 of the illustrated example includes alogging-while-drilling (LWD) module 254, a measuring-while-drilling(MWD) module 256, an optional module 258, a roto-steerable system andmotor 260, and the drill bit 226. Such components or modules may bereferred to as tools where a drillstring can include a plurality oftools.

The LWD module 254 may be housed in a suitable type of drill collar andcan contain one or a plurality of selected types of logging tools. Itwill also be understood that more than one LWD and/or MWD module can beemployed, for example, as represented at by the module 256 of thedrillstring assembly 250. Where the position of an LWD module ismentioned, as an example, it may refer to a module at the position ofthe LWD module 254, the module 256, etc. An LWD module can includecapabilities for measuring, processing, and storing information, as wellas for communicating with the surface equipment. In the illustratedexample, the LWD module 254 may include a seismic measuring device.

The MWD module 256 may be housed in a suitable type of drill collar andcan contain one or more devices for measuring characteristics of thedrillstring 225 and the drill bit 226. As an example, the MWD tool 254may include equipment for generating electrical power, for example, topower various components of the drillstring 225. As an example, the MWDtool 254 may include the telemetry equipment 252, for example, where theturbine impeller can generate power by flow of the mud; it beingunderstood that other power and/or battery systems may be employed forpurposes of powering various components. As an example, the MWD module256 may include one or more of the following types of measuring devices:a weight-on-bit measuring device, a torque measuring device, a vibrationmeasuring device, a shock measuring device, a stick slip measuringdevice, a direction measuring device, and an inclination measuringdevice.

FIG. 2 also shows some examples of types of holes that may be drilled.For example, consider a slant hole 272, an S-shaped hole 274, a deepinclined hole 276 and a horizontal hole 278.

As an example, a drilling operation can include directional drillingwhere, for example, at least a portion of a well includes a curved axis.For example, consider a radius that defines curvature where aninclination with regard to the vertical may vary until reaching an anglebetween about 30 degrees and about 60 degrees or, for example, an angleto about 90 degrees or possibly greater than about 90 degrees.

As an example, a directional well can include several shapes where eachof the shapes may aim to meet particular operational demands. As anexample, a drilling process may be performed on the basis of informationas and when it is relayed to a drilling engineer. As an example,inclination and/or direction may be modified based on informationreceived during a drilling process.

As an example, deviation of a bore may be accomplished in part by use ofa downhole motor and/or a turbine. As to a motor, for example, adrillstring can include a positive displacement motor (PDM).

As an example, a system may be a steerable system and include equipmentto perform a method such as geosteering. As an example, a steerablesystem can include a PDM or a turbine on a lower part of a drillstringwhich, just above a drill bit, a bent sub can be mounted. As an example,above a PDM, MWD equipment that provides real time or near real timedata of interest (e.g., inclination, direction, pressure, temperature,real weight on the drill bit, torque stress, etc.) and/or LWD equipmentmay be installed. As to the latter, LWD equipment can make it possibleto send to the surface various types of data of interest, including forexample, geological data (e.g., gamma ray log, resistivity, density andsonic logs, etc.).

The coupling of sensors providing information on the course of a welltrajectory, in real time or near real time, with, for example, one ormore logs characterizing the formations from a geological viewpoint, canallow for implementing a geosteering method. Such a method can includenavigating a subsurface environment, for example, to follow a desiredroute to reach a desired target or targets.

As an example, a drillstring can include an azimuthal density neutron(ADN) tool for measuring density and porosity; a MWD tool for measuringinclination, azimuth and shocks; a compensated dual resistivity (CDR)tool for measuring resistivity and gamma ray related phenomena; one ormore variable gauge stabilizers; one or more bend joints; and ageosteering tool, which may include a motor and optionally equipment formeasuring and/or responding to one or more of inclination, resistivityand gamma ray related phenomena.

As an example, geosteering can include intentional directional controlof a wellbore based on results of downhole geological loggingmeasurements in a manner that aims to keep a directional wellbore withina desired region, zone (e.g., a pay zone), etc. As an example,geosteering may include directing a wellbore to keep the wellbore in aparticular section of a reservoir, for example, to minimize gas and/orwater breakthrough and, for example, to maximize economic productionfrom a well that includes the wellbore.

Referring again to FIG. 2 , the wellsite system 200 can include one ormore sensors 264 that are operatively coupled to the control and/or dataacquisition system 262. As an example, a sensor or sensors may be atsurface locations. As an example, a sensor or sensors may be at downholelocations. As an example, a sensor or sensors may be at one or moreremote locations that are not within a distance of the order of aboutone hundred meters from the wellsite system 200. As an example, a sensoror sensor may be at an offset wellsite where the wellsite system 200 andthe offset wellsite are in a common field (e.g., oil and/or gas field).

As an example, one or more of the sensors 264 can be provided fortracking pipe, tracking movement of at least a portion of a drillstring,etc.

As an example, the system 200 can include one or more sensors 266 thatcan sense and/or transmit signals to a fluid conduit such as a drillingfluid conduit (e.g., a drilling mud conduit). For example, in the system200, the one or more sensors 266 can be operatively coupled to portionsof the standpipe 208 through which mud flows. As an example, a downholetool can generate pulses that can travel through the mud and be sensedby one or more of the one or more sensors 266. In such an example, thedownhole tool can include associated circuitry such as, for example,encoding circuitry that can encode signals, for example, to reducedemands as to transmission. As an example, circuitry at the surface mayinclude decoding circuitry to decode encoded information transmitted atleast in part via mud-pulse telemetry. As an example, circuitry at thesurface may include encoder circuitry and/or decoder circuitry andcircuitry downhole may include encoder circuitry and/or decodercircuitry. As an example, the system 200 can include a transmitter thatcan generate signals that can be transmitted downhole via mud (e.g.,drilling fluid) as a transmission medium.

As an example, one or more portions of a drillstring may become stuck.The term stuck can refer to one or more of varying degrees of inabilityto move or remove a drillstring from a bore. As an example, in a stuckcondition, it might be possible to rotate pipe or lower it back into abore or, for example, in a stuck condition, there may be an inability tomove the drillstring axially in the bore, though some amount of rotationmay be possible. As an example, in a stuck condition, there may be aninability to move at least a portion of the drillstring axially androtationally.

As to the term “stuck pipe”, this can refer to a portion of adrillstring that cannot be rotated or moved axially. As an example, acondition referred to as “differential sticking” can be a conditionwhereby the drillstring cannot be moved (e.g., rotated or reciprocated)along the axis of the bore. Differential sticking may occur whenhigh-contact forces caused by low reservoir pressures, high wellborepressures, or both, are exerted over a sufficiently large area of thedrillstring. Differential sticking can have time and financial cost.

As an example, a sticking force can be a product of the differentialpressure between the wellbore and the reservoir and the area that thedifferential pressure is acting upon. This means that a relatively lowdifferential pressure (delta p) applied over a large working area can bejust as effective in sticking pipe as can a high differential pressureapplied over a small area.

As an example, a condition referred to as “mechanical sticking” can be acondition where limiting or prevention of motion of the drillstring by amechanism other than differential pressure sticking occurs. Mechanicalsticking can be caused, for example, by one or more of junk in the hole,wellbore geometry anomalies, cement, keyseats or a buildup of cuttingsin the annulus.

FIG. 3 shows an example of a system 300 that includes various equipmentfor evaluation 310, planning 320, engineering 330 and operations 340.For example, a drilling workflow framework 301, a seismic-to-simulationframework 302, a technical data framework 303 and a drilling framework304 may be implemented to perform one or more processes such as aevaluating a formation 314, evaluating a process 318, generating atrajectory 324, validating a trajectory 328, formulating constraints334, designing equipment and/or processes based at least in part onconstraints 338, performing drilling 344 and evaluating drilling and/orformation 348.

In the example of FIG. 3 , the seismic-to-simulation framework 302 canbe, for example, the PETREL® framework (Schlumberger Limited, Houston,Tex.) and the technical data framework 303 can be, for example, theTECHLOG® framework (Schlumberger Limited, Houston, Tex.).

As an example, a framework can include entities that may include earthentities, geological objects or other objects such as wells, surfaces,reservoirs, etc. Entities can include virtual representations of actualphysical entities that are reconstructed for purposes of one or more ofevaluation, planning, engineering, operations, etc.

Entities may include entities based on data acquired via sensing,observation, etc. (e.g., seismic data and/or other information). Anentity may be characterized by one or more properties (e.g., ageometrical pillar grid entity of an earth model may be characterized bya porosity property). Such properties may represent one or moremeasurements (e.g., acquired data), calculations, etc.

A framework may be an object-based framework. In such a framework,entities may include entities based on pre-defined classes, for example,to facilitate modeling, analysis, simulation, etc. A commerciallyavailable example of an object-based framework is the MICROSOFT™ .NET™framework (Redmond, Wash.), which provides a set of extensible objectclasses. In the .NET™ framework, an object class encapsulates a moduleof reusable code and associated data structures. Object classes can beused to instantiate object instances for use in by a program, script,etc. For example, borehole classes may define objects for representingboreholes based on well data.

As an example, a framework can include an analysis component that mayallow for interaction with a model or model-based results (e.g.,simulation results, etc.). As to simulation, a framework may operativelylink to or include a simulator such as the ECLIPSE® reservoir simulator(Schlumberger Limited, Houston Tex.), the INTERSECT® reservoir simulator(Schlumberger Limited, Houston Tex.), etc.

The aforementioned PETREL® framework provides components that allow foroptimization of exploration and development operations. The PETREL®framework includes seismic to simulation software components that canoutput information for use in increasing reservoir performance, forexample, by improving asset team productivity. Through use of such aframework, various professionals (e.g., geophysicists, geologists, wellengineers, reservoir engineers, etc.) can develop collaborativeworkflows and integrate operations to streamline processes. Such aframework may be considered an application and may be considered adata-driven application (e.g., where data is input for purposes ofmodeling, simulating, etc.).

As an example, one or more frameworks may be interoperative and/or runupon one or another. As an example, consider the commercially availableframework environment marketed as the OCEAN® framework environment(Schlumberger Limited, Houston, Tex.), which allows for integration ofadd-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN®framework environment leverages .NET™ tools (Microsoft Corporation,Redmond, Wash.) and offers stable, user-friendly interfaces forefficient development. In an example embodiment, various components maybe implemented as add-ons (or plug-ins) that conform to and operateaccording to specifications of a framework environment (e.g., accordingto application programming interface (API) specifications, etc.).

As an example, a framework can include a model simulation layer alongwith a framework services layer, a framework core layer and a moduleslayer. The framework may include the commercially available OCEAN®framework where the model simulation layer can include or operativelylink to the commercially available PETREL® model-centric softwarepackage that hosts OCEAN® framework applications. In an exampleembodiment, the PETREL® software may be considered a data-drivenapplication. The PETREL® software can include a framework for modelbuilding and visualization. Such a model may include one or more grids.

As an example, the model simulation layer may provide domain objects,act as a data source, provide for rendering and provide for various userinterfaces. Rendering may provide a graphical environment in whichapplications can display their data while the user interfaces mayprovide a common look and feel for application user interfacecomponents.

As an example, domain objects can include entity objects, propertyobjects and optionally other objects. Entity objects may be used togeometrically represent wells, surfaces, reservoirs, etc., whileproperty objects may be used to provide property values as well as dataversions and display parameters. For example, an entity object mayrepresent a well where a property object provides log information aswell as version information and display information (e.g., to displaythe well as part of a model).

As an example, data may be stored in one or more data sources (or datastores, generally physical data storage devices), which may be at thesame or different physical sites and accessible via one or morenetworks. As an example, a model simulation layer may be configured tomodel projects. As such, a particular project may be stored where storedproject information may include inputs, models, results and cases. Thus,upon completion of a modeling session, a user may store a project. At alater time, the project can be accessed and restored using the modelsimulation layer, which can recreate instances of the relevant domainobjects.

As an example, the system 300 may be used to perform one or moreworkflows. A workflow may be a process that includes a number ofworksteps. A workstep may operate on data, for example, to create newdata, to update existing data, etc. As an example, a workflow mayoperate on one or more inputs and create one or more results, forexample, based on one or more algorithms. As an example, a system mayinclude a workflow editor for creation, editing, executing, etc. of aworkflow. In such an example, the workflow editor may provide forselection of one or more pre-defined worksteps, one or more customizedworksteps, etc. As an example, a workflow may be a workflowimplementable at least in part in the PETREL® software, for example,that operates on seismic data, seismic attribute(s), etc.

As an example, seismic data can be data acquired via a seismic surveywhere sources and receivers are positioned in a geologic environment toemit and receive seismic energy where at least a portion of such energycan reflect off subsurface structures. As an example, a seismic dataanalysis framework or frameworks (e.g., consider the OMEGA® framework,marketed by Schlumberger Limited, Houston, Tex.) may be utilized todetermine depth, extent, properties, etc. of subsurface structures. Asan example, seismic data analysis can include forward modeling and/orinversion, for example, to iteratively build a model of a subsurfaceregion of a geologic environment. As an example, a seismic data analysisframework may be part of or operatively coupled to aseismic-to-simulation framework (e.g., the PETREL® framework, etc.).

As an example, a workflow may be a process implementable at least inpart in the OCEAN® framework. As an example, a workflow may include oneor more worksteps that access a module such as a plug-in (e.g., externalexecutable code, etc.).

As an example, a framework may provide for modeling petroleum systems.For example, the commercially available modeling framework marketed asthe PETROMOD® framework (Schlumberger Limited, Houston, Tex.) includesfeatures for input of various types of information (e.g., seismic, well,geological, etc.) to model evolution of a sedimentary basin. ThePETROMOD® framework provides for petroleum systems modeling via input ofvarious data such as seismic data, well data and other geological data,for example, to model evolution of a sedimentary basin. The PETROMOD®framework may predict if, and how, a reservoir has been charged withhydrocarbons, including, for example, the source and timing ofhydrocarbon generation, migration routes, quantities, pore pressure andhydrocarbon type in the subsurface or at surface conditions. Incombination with a framework such as the PETREL® framework, workflowsmay be constructed to provide basin-to-prospect scale explorationsolutions. Data exchange between frameworks can facilitate constructionof models, analysis of data (e.g., PETROMOD® framework data analyzedusing PETREL® framework capabilities), and coupling of workflows.

As mentioned, a drillstring can include various tools that may makemeasurements. As an example, a wireline tool or another type of tool maybe utilized to make measurements. As an example, a tool may beconfigured to acquire electrical borehole images. As an example, thefullbore Formation MicroImager (FMI) tool (Schlumberger Limited,Houston, Tex.) can acquire borehole image data. A data acquisitionsequence for such a tool can include running the tool into a boreholewith acquisition pads closed, opening and pressing the pads against awall of the borehole, delivering electrical current into the materialdefining the borehole while translating the tool in the borehole, andsensing current remotely, which is altered by interactions with thematerial.

Analysis of formation information may reveal features such as, forexample, vugs, dissolution planes (e.g., dissolution along beddingplanes), stress-related features, dip events, etc. As an example, a toolmay acquire information that may help to characterize a reservoir,optionally a fractured reservoir where fractures may be natural and/orartificial (e.g., hydraulic fractures). As an example, informationacquired by a tool or tools may be analyzed using a framework such asthe TECHLOG® framework. As an example, the TECHLOG® framework can beinteroperable with one or more other frameworks such as, for example,the PETREL® framework.

As an example, various aspects of a workflow may be completedautomatically, may be partially automated, or may be completed manually,as by a human user interfacing with a software application. As anexample, a workflow may be cyclic, and may include, as an example, fourstages such as, for example, an evaluation stage (see, e.g., theevaluation equipment 310), a planning stage (see, e.g., the planningequipment 320), an engineering stage (see, e.g., the engineeringequipment 330) and an execution stage (see, e.g., the operationsequipment 340). As an example, a workflow may commence at one or morestages, which may progress to one or more other stages (e.g., in aserial manner, in a parallel manner, in a cyclical manner, etc.).

As an example, a workflow can commence with an evaluation stage, whichmay include a geological service provider evaluating a formation (see,e.g., the evaluation block 314). As an example, a geological serviceprovider may undertake the formation evaluation using a computing systemexecuting a software package tailored to such activity; or, for example,one or more other suitable geology platforms may be employed (e.g.,alternatively or additionally). As an example, the geological serviceprovider may evaluate the formation, for example, using earth models,geophysical models, basin models, petrotechnical models, combinationsthereof, and/or the like. Such models may take into consideration avariety of different inputs, including offset well data, seismic data,pilot well data, other geologic data, etc. The models and/or the inputmay be stored in the database maintained by the server and accessed bythe geological service provider.

As an example, a workflow may progress to a geology and geophysics(“G&G”) service provider, which may generate a well trajectory (see,e.g., the generation block 324), which may involve execution of one ormore G&G software packages. Examples of such software packages includethe PETREL® framework. As an example, a G&G service provider maydetermine a well trajectory or a section thereof, based on, for example,one or more model(s) provided by a formation evaluation (e.g., per theevaluation block 314), and/or other data, e.g., as accessed from one ormore databases (e.g., maintained by one or more servers, etc.). As anexample, a well trajectory may take into consideration various “basis ofdesign” (BOD) constraints, such as general surface location, target(e.g., reservoir) location, and the like. As an example, a trajectorymay incorporate information about tools, bottom-hole assemblies, casingsizes, etc., that may be used in drilling the well. A well trajectorydetermination may take into consideration a variety of other parameters,including risk tolerances, fluid weights and/or plans, bottom-holepressures, drilling time, etc.

As an example, a workflow may progress to a first engineering serviceprovider (e.g., one or more processing machines associated therewith),which may validate a well trajectory and, for example, relief welldesign (see, e.g., the validation block 328). Such a validation processmay include evaluating physical properties, calculations, risktolerances, integration with other aspects of a workflow, etc. As anexample, one or more parameters for such determinations may bemaintained by a server and/or by the first engineering service provider;noting that one or more model(s), well trajectory(ies), etc. may bemaintained by a server and accessed by the first engineering serviceprovider. For example, the first engineering service provider mayinclude one or more computing systems executing one or more softwarepackages. As an example, where the first engineering service providerrejects or otherwise suggests an adjustment to a well trajectory, thewell trajectory may be adjusted or a message or other notification sentto the G&G service provider requesting such modification.

As an example, one or more engineering service providers (e.g., first,second, etc.) may provide a casing design, bottom-hole assembly (BHA)design, fluid design, and/or the like, to implement a well trajectory(see, e.g., the design block 338). In some embodiments, a secondengineering service provider may perform such design using one of moresoftware applications. Such designs may be stored in one or moredatabases maintained by one or more servers, which may, for example,employ STUDIO® framework tools, and may be accessed by one or more ofthe other service providers in a workflow.

As an example, a second engineering service provider may seek approvalfrom a third engineering service provider for one or more designsestablished along with a well trajectory. In such an example, the thirdengineering service provider may consider various factors as to whetherthe well engineering plan is acceptable, such as economic variables(e.g., oil production forecasts, costs per barrel, risk, drill time,etc.), and may request authorization for expenditure, such as from theoperating company's representative, well-owner's representative, or thelike (see, e.g., the formulation block 334). As an example, at leastsome of the data upon which such determinations are based may be storedin one or more database maintained by one or more servers. As anexample, a first, a second, and/or a third engineering service providermay be provided by a single team of engineers or even a single engineer,and thus may or may not be separate entities.

As an example, where economics may be unacceptable or subject toauthorization being withheld, an engineering service provider maysuggest changes to casing, a bottom-hole assembly, and/or fluid design,or otherwise notify and/or return control to a different engineeringservice provider, so that adjustments may be made to casing, abottom-hole assembly, and/or fluid design. Where modifying one or moreof such designs is impracticable within well constraints, trajectory,etc., the engineering service provider may suggest an adjustment to thewell trajectory and/or a workflow may return to or otherwise notify aninitial engineering service provider and/or a G&G service provider suchthat either or both may modify the well trajectory.

As an example, a workflow can include considering a well trajectory,including an accepted well engineering plan, and a formation evaluation.Such a workflow may then pass control to a drilling service provider,which may implement the well engineering plan, establishing safe andefficient drilling, maintaining well integrity, and reporting progressas well as operating parameters (see, e.g., the blocks 344 and 348). Asan example, operating parameters, formation encountered, data collectedwhile drilling (e.g., using logging-while-drilling ormeasuring-while-drilling technology), may be returned to a geologicalservice provider for evaluation. As an example, the geological serviceprovider may then re-evaluate the well trajectory, or one or more otheraspects of the well engineering plan, and may, in some cases, andpotentially within predetermined constraints, adjust the wellengineering plan according to the real-life drilling parameters (e.g.,based on acquired data in the field, etc.).

Whether the well is entirely drilled, or a section thereof is completed,depending on the specific embodiment, a workflow may proceed to a postreview (see, e.g., the evaluation block 318). As an example, a postreview may include reviewing drilling performance. As an example, a postreview may further include reporting the drilling performance (e.g., toone or more relevant engineering, geological, or G&G service providers).

Various activities of a workflow may be performed consecutively and/ormay be performed out of order (e.g., based partially on information fromtemplates, nearby wells, etc. to fill in any gaps in information that isto be provided by another service provider). As an example, undertakingone activity may affect the results or basis for another activity, andthus may, either manually or automatically, call for a variation in oneor more workflow activities, work products, etc. As an example, a servermay allow for storing information on a central database accessible tovarious service providers where variations may be sought bycommunication with an appropriate service provider, may be madeautomatically, or may otherwise appear as suggestions to the relevantservice provider. Such an approach may be considered to be a holisticapproach to a well workflow, in comparison to a sequential, piecemealapproach.

As an example, various actions of a workflow may be repeated multipletimes during drilling of a wellbore. For example, in one or moreautomated systems, feedback from a drilling service provider may beprovided at or near real-time, and the data acquired during drilling maybe fed to one or more other service providers, which may adjust itspiece of the workflow accordingly. As there may be dependencies in otherareas of the workflow, such adjustments may permeate through theworkflow, e.g., in an automated fashion. In some embodiments, a cyclicprocess may additionally or instead proceed after a certain drillinggoal is reached, such as the completion of a section of the wellbore,and/or after the drilling of the entire wellbore, or on a per-day, week,month, etc. basis.

Well planning can include determining a path of a well that can extendto a reservoir, for example, to economically produce fluids such ashydrocarbons therefrom. Well planning can include selecting a drillingand/or completion assembly which may be used to implement a well plan.As an example, various constraints can be imposed as part of wellplanning that can impact design of a well. As an example, suchconstraints may be imposed based at least in part on information as toknown geology of a subterranean domain, presence of one or more otherwells (e.g., actual and/or planned, etc.) in an area (e.g., considercollision avoidance), etc. As an example, one or more constraints may beimposed based at least in part on characteristics of one or more tools,components, etc. As an example, one or more constraints may be based atleast in part on factors associated with drilling time and/or risktolerance.

As an example, a system can allow for a reduction in waste, for example,as may be defined according to LEAN. In the context of LEAN, considerone or more of the following types of waste: transport (e.g., movingitems unnecessarily, whether physical or data); inventory (e.g.,components, whether physical or informational, as work in process, andfinished product not being processed); motion (e.g., people or equipmentmoving or walking unnecessarily to perform desired processing); waiting(e.g., waiting for information, interruptions of production during shiftchange, etc.); overproduction (e.g., production of material,information, equipment, etc. ahead of demand); over Processing (e.g.,resulting from poor tool or product design creating activity); anddefects (e.g., effort involved in inspecting for and fixing defectswhether in a plan, data, equipment, etc.). As an example, a system thatallows for actions (e.g., methods, workflows, etc.) to be performed in acollaborative manner can help to reduce one or more types of waste.

As an example, a system can be utilized to implement a method forfacilitating distributed well engineering, planning, and/or drillingsystem design across multiple computation devices where collaborationcan occur among various different users (e.g., some being local, somebeing remote, some being mobile, etc.). In such a system, the varioususers via appropriate devices may be operatively coupled via one or morenetworks (e.g., local and/or wide area networks, public and/or privatenetworks, land-based, marine-based and/or areal networks, etc.).

As an example, a system may allow well engineering, planning, and/ordrilling system design to take place via a subsystems approach where awellsite system is composed of various subsystem, which can includeequipment subsystems and/or operational subsystems (e.g., controlsubsystems, etc.). As an example, computations may be performed usingvarious computational platforms/devices that are operatively coupled viacommunication links (e.g., network links, etc.). As an example, one ormore links may be operatively coupled to a common database (e.g., aserver site, etc.). As an example, a particular server or servers maymanage receipt of notifications from one or more devices and/or issuanceof notifications to one or more devices. As an example, a system may beimplemented for a project where the system can output a well plan, forexample, as a digital well plan, a paper well plan, a digital and paperwell plan, etc. Such a well plan can be a complete well engineering planor design for the particular project.

FIG. 4 shows an example of a system 400 that includes various componentsthat can be local to a wellsite and includes various components that canbe remote from a wellsite. As shown, the system 400 includes a Maestroblock 402, an Opera block 404, a Core & Services block 406 and anEquipment block 408. These blocks can be labeled in one or more mannersother than as shown in the example of FIG. 4 . In the example of FIG. 4, the blocks 402, 404, 406 and 408 can be defined by one or more ofoperational features, functions, relationships in an architecture, etc.

As an example, the blocks 402, 404, 406 and 408 may be described in apyramidal architecture where, from peak to base, a pyramid includes theMaestro block 402, the Opera block 404, the Core & Services block 406and the Equipment block 408.

As an example, the Maestro block 402 can be associated with a wellmanagement level (e.g., well planning and/or orchestration) and can beassociated with a rig management level (e.g., rig dynamic planningand/or orchestration). As an example, the Opera block 404 can beassociated with a process management level (e.g., rig integratedexecution). As an example, the Core & Services block 406 can beassociated with a data management level (e.g., sensor, instrumentation,inventory, etc.). As an example, the Equipment block 408 can beassociated with a wellsite equipment level (e.g., wellsite subsystems,etc.).

As an example, the Maestro block 402 may receive information from adrilling workflow framework and/or one or more other sources, which maybe remote from a wellsite.

In the example of FIG. 4 , the Maestro block 402 includes a plan/replanblock 422, an orchestrate/arbitrate block 424 and a local resourcemanagement block 426. In the example of FIG. 4 , the Opera block 404includes an integrated execution block 444, which can include or beoperatively coupled to blocks for various subsystems of a wellsite suchas a drilling subsystem, a mud management subsystem (e.g., a hydraulicssubsystem), a casing subsystem (e.g., casings and/or completionssubsystem), and, for example, one or more other subsystems. In theexample of FIG. 4 , the Core & Services block 406 includes a datamanagement and real-time services block 464 (e.g., real-time or nearreal-time services) and a rig and cloud security block 468 (e.g., as toprovisioning and various type of security measures, etc.). In theexample of FIG. 4 , the Equipment block 408 is shown as being capable ofproviding various types of information to the Core & Services block 406.For example, consider information from a rig surface sensor, a LWD/MWDsensor, a mud logging sensor, a rig control system, rig equipment,personnel, material, etc. In the example, of FIG. 4 , a block 470 canprovide for one or more of data visualization, automatic alarms,automatic reporting, etc. As an example, the block 470 may beoperatively coupled to the Core & Services block 406 and/or one or moreother blocks.

As mentioned, a portion of the system 400 can be remote from a wellsite.For example, to one side of a dashed line appear a remote operationcommand center block 492, a database block 493, a drilling workflowframework block 494, a SAP/ERP block 495 and a field services deliveryblock 496. Various blocks that may be remote can be operatively coupledto one or more blocks that may be local to a wellsite system. Forexample, a communication link 412 is illustrated in the example of FIG.4 that can operatively couple the blocks 406 and 492 (e.g., as tomonitoring, remote control, etc.), while another communication link 414is illustrated in the example of FIG. 4 that can operatively couple theblocks 406 and 496 (e.g., as to equipment delivery, equipment services,etc.). Various other examples of possible communication links are alsoillustrated in the example of FIG. 4 .

As an example, the system 400 of FIG. 4 may be a field management tool.As an example, the system 400 of FIG. 4 may include a drilling framework(see, e.g., the drilling framework 304). As an example, blocks in thesystem 400 of FIG. 4 that may be remote from a wellsite.

As an example, a wellbore can be drilled according to a drilling planthat is established prior to drilling. Such a drilling plan, which maybe a well plan or a portion thereof, can set forth equipment, pressures,trajectories and/or other parameters that define drilling process for awellsite. As an example, a drilling operation may then be performedaccording to the drilling plan (e.g., well plan). As an example, asinformation is gathered, a drilling operation may deviate from adrilling plan. Additionally, as drilling or other operations areperformed, subsurface conditions may change. Specifically, as newinformation is collected, sensors may transmit data to one or moresurface units. As an example, a surface unit may automatically use suchdata to update a drilling plan (e.g., locally and/or remotely).

As an example, the drilling workflow framework 494 can be or include aG&G system and a well planning system. As an example, a G&G systemcorresponds to hardware, software, firmware, or a combination thereofthat provides support for geology and geophysics. In other words, ageologist who understands the reservoir may decide where to drill thewell using the G&G system that creates a three-dimensional model of thesubsurface formation and includes simulation tools. The G&G system maytransfer a well trajectory and other information selected by thegeologist to a well planning system. The well planning systemcorresponds to hardware, software, firmware, or a combination thereofthat produces a well plan. In other words, the well plan may be ahigh-level drilling program for the well. The well planning system mayalso be referred to as a well plan generator.

In the example of FIG. 4 , various blocks can be components that maycorrespond to one or more software modules, hardware infrastructure,firmware, equipment, or any combination thereof. Communication betweenthe components may be local or remote, direct or indirect, viaapplication programming interfaces, and procedure calls, or through oneor more communication channels.

As an example, various blocks in the system 400 of FIG. 4 can correspondto levels of granularity in controlling operations of associated withequipment and/or personnel in an oilfield. As shown in FIG. 4 , thesystem 400 can include the Maestro block 402 (e.g., for well planexecution), the Opera block 404 (e.g., process manager collection), theCore & Services block 406, and the Equipment block 408.

The Maestro block 402 may be referred to as a well plan executionsystem. For example, a well plan execution system corresponds tohardware, software, firmware or a combination thereof that performs anoverall coordination of the well construction process, such ascoordination of a drilling rig and the management of the rig and the rigequipment. A well plan execution system may be configured to obtain thegeneral well plan from well planning system and transform the generalwell plan into a detailed well plan. The detailed well plan may includea specification of the activities involved in performing an action inthe general well plan, the days and/or times to perform the activities,the individual resources performing the activities, and otherinformation.

As an example, a well plan execution system may further includefunctionality to monitor an execution of a well plan to track progressand dynamically adjust the plan. Further, a well plan execution systemmay be configured to handle logistics and resources with respect to onand off the rig. As an example, a well plan execution system may includemultiple sub-components, such as a detailer that is configured to detailthe well planning system plan, a monitor that is configured to monitorthe execution of the plan, a plan manager that is configured to performdynamic plan management, and a logistics and resources manager tocontrol the logistics and resources of the well. In one or moreembodiments, a well plan execution system may be configured tocoordinate between the different processes managed by a process managercollection (see, e.g., the Opera block 404). In other words, a well planexecution system can communicate and manage resource sharing betweenprocesses in a process manager collection while operating at, forexample, a higher level of granularity than process manager collection.

As to the Opera block 404, as mentioned, it may be referred to as aprocess manager collection. In one or more embodiments, a processmanager collection can include functionality to perform individualprocess management of individual domains of an oilfield, such as a rig.For example, when drilling a well, different activities may beperformed. Each activity may be controlled by an individual processmanager in the process manager collection. A process manager collectionmay include multiple process managers, whereby each process managercontrols a different activity (e.g., activity related to the rig). Inother words, each process manager may have a set of tasks defined forthe process manager that is particular to the type of physics involvedin the activity. For example, drilling a well may use drilling mud,which is fluid pumped into well in order to extract drill cuttings fromthe well. A drilling mud process manager may exist in a process managercollection that manages the mixing of the drilling mud, the composition,testing of the drilling mud properties, determining whether the pressureis accurate, and performing other such tasks. The drilling mud processmanager may be separate from a process manager that controls movement ofdrill pipe from a well. Thus, a process manager collection may partitionactivities into several different domains and manages each of thedomains individually. Amongst other possible process managers, a processmanager collection may include, for example, a drilling process manager,a mud preparation and management process manager, a casing runningprocess manager, a cementing process manager, a rig equipment processmanager, and other process managers. Further, a process managercollection may provide direct control or advice regarding the componentsabove. As an example, coordination between process managers in a processmanager collection may be performed by a well plan execution system.

As to the Core & Service block 406 (e.g., a core services block or CSblock), it can include functionality to manage individual pieces ofequipment and/or equipment subsystems. As an example, a CS block caninclude functionality to handle basic data structure of the oilfield,such as the rig, acquire metric data, produce reports, and managesresources of people and supplies. As an example, a CS block may includea data acquirer and aggregator, a rig state identifier, a real-time (RT)drill services (e.g., near real-time), a reporter, a cloud, and aninventory manager.

As an example, a data acquirer and aggregator can include functionalityto interface with individual equipment components and sensor and acquiredata. As an example, a data acquirer and aggregator may further includefunctionality to interface with sensors located at the oilfield.

As an example, a rig state identifier can includes functionality toobtain data from the data acquirer and aggregator and transform the datainto state information. As an example, state information may includehealth and operability of a rig as well as information about aparticular task being performed by equipment.

As an example, RT drill services can include functionality to transmitand present information to individuals. In particular, the RT drillservices can include functionality to transmit information toindividuals involved according to roles and, for example, device typesof each individual (e.g., mobile, desktop, etc.). In one or moreembodiments, information presented by RT drill services can be contextspecific, and may include a dynamic display of information so that ahuman user may view details about items of interest.

As an example, in one or more embodiments, a reporter can includefunctionality to generate reports. For example, reporting may be basedon requests and/or automatic generation and may provide informationabout state of equipment and/or people.

As an example, a wellsite “cloud” framework can correspond to aninformation technology infrastructure locally at an oilfield, such as anindividual rig in the oilfield. In such an example, the wellsite “cloud”framework may be an “Internet of Things” (IoT) framework. As an example,a wellsite “cloud” framework can be an edge of the cloud (e.g., anetwork of networks) or of a private network.

As an example, an inventory manager can be a block that includesfunctionality to manage materials, such as a list and amount of eachresource on a rig.

In the example of FIG. 4 , the Equipment block 408 can correspond tovarious controllers, control unit, control equipment, etc. that may beoperatively coupled to and/or embedded into physical equipment at awellsite such as, for example, rig equipment. For example, the Equipmentblock 408 may correspond to software and control systems for individualitems on the rig. As an example, the Equipment block 408 may provide formonitoring sensors from multiple subsystems of a drilling rig andprovide control commands to multiple subsystem of the drilling rig, suchthat sensor data from multiple subsystems may be used to provide controlcommands to the different subsystems of the drilling rig and/or otherdevices, etc. For example, a system may collect temporally and depthaligned surface data and downhole data from a drilling rig and transmitthe collected data to data acquirers and aggregators in core services,which can store the collected data for access onsite at a drilling rigor offsite via a computing resource environment.

As mentioned, the system 400 of FIG. 4 can be associated with a planwhere, for example, the plan/replan block 422 can provide for planningand/or re-planning one or more operations, etc.

FIG. 5 shows an example of a graphical user interface (GUI) 500 thatincludes information associated with a well plan. Specifically, the GUI500 includes a panel 510 where surfaces representations 512 and 514 arerendered along with well trajectories where a location 516 can representa position of a drillstring 517 along a well trajectory. The GUI 500 mayinclude one or more editing features such as an edit well plan set offeatures 530. The GUI 500 may include information as to individuals of ateam 540 that are involved, have been involved and/or are to be involvedwith one or more operations. The GUI 500 may include information as toone or more activities 550. As shown in the example of FIG. 5 , the GUI500 can include a graphical control of a drillstring 560 where, forexample, various portions of the drillstring 560 may be selected toexpose one or more associated parameters (e.g., type of equipment,equipment specifications, operational history, etc.). FIG. 5 also showsa table 570 as a point spreadsheet that specifies information for aplurality of wells.

FIG. 6 shows an example of a graphical user interface (GUI) 600 thatincludes a calendar with dates for various operations that can be partof a plan. For example, the GUI 600 shows rig up, casing, cement,drilling and rig down operations that can occur over various periods oftime. Such a GUI may be editable via selection of one or more graphicalcontrols.

As explained, a plan can include various plan parameters, which mayrepresent plan states at one or more points in time. As an example, amethod can include representing a plan as a two-dimensional orthree-dimensional data structure. For example, consider a method thatincludes representing a plan as a two-dimensional array such as a pixelarray of a two-dimensional image. As an example, a three-dimensionalarray may be a voxel array where such an array may optionally be slicedto generate one or more two-dimensional arrays.

As an example, a method can include converting plan inputs intopixel-grids that can be interpreted as images. In such an example, thepixel-grids can be given as input to a convolutional neuralnetwork-based image interpretation processes. In such an example, acomputational framework can recognize a state of a physical system suchas a rig system, etc. Such a method may optionally be implemented viaone or more portions of a system such as the system 400 of FIG. 4 . Forexample, the Maestro block 402 of the system 400 can include componentsfor implementation of a method that include image-based interpretation,which may generate one or more outputs (e.g., control outputs, planningoutputs, etc.).

A convolutional neural network (CNN) can be a class of deep,feed-forward artificial neural networks that is suitable for analyzingvisual imagery. As mentioned, for one or more field operations, variousstates can exist where one or more states may be represented as an imageor images. Such an image or images can be structured for analysis ratherthan visual inspection by the human eye. For example, a CNN may beapplied to recognizing images of animals as in pixel-based photographswhere a human can confirm via visual inspection that an input image of acow has been properly classified by the CNN to be an image of a cow;whereas, for field operations (e.g., planning, execution, etc.), apixel-based image may have little recognizable information to a humanvia visual inspection, yet a CNN can be utilized, for example, toclassify the pixel-based image as corresponding to one or more states ofone or more field operations (e.g., equipment, flows, rock conditions,etc.). Such a pixel-based image may be representative of a single pointin time or may be representative of multiple points in time. In eitherinstance, a CNN approach may be implemented for one or more purposes.

Terminology as to CNNs tends to involve visual or perception descriptorswhere, herein, such descriptors can be applied to analysis of what maybe considered “non-image” data that are organized in an image format(e.g., in a pixel-based format, etc.).

CNNs can use a variation of multilayer perceptrons designed for minimalpreprocessing. CNNs may be shift invariant or space invariant artificialneural networks (SIANN), based on their shared-weights architecture andtranslation invariance characteristics.

Convolutional neural networks, as neural networks generally, find abasis within biological processes in which a connectivity patternbetween neurons is inspired by organization of an animal's visualcortex. For example, individual cortical neurons tend to respond tostimuli in a restricted region of a visual field known as the receptivefield where receptive fields of different neurons partially overlap suchthat they cover the entire visual field within an animal's view.

CNNs tend to use relatively little pre-processing compared to some othertypes of image classification algorithms. As such, a network learnsfilters that in other approaches may have been hand-engineered.Independence from prior knowledge and human effort in feature design canbe beneficial.

As an example, a method can include one or more deep neural networksthat can be trained, for example, to provide domain specific heuristics.Such heuristics may provide for improved planning efficiency, robustnessand/or adaptability. For example, consider a method where results oflearning using deep neural networks are used to guide heuristic searchduring plan generation, for example, using a learned policy network tolimit width of search and a learned value network to limit depth ofsearch.

FIG. 7 shows an example of a system 700 that includes a deep neuralnetwork (DNN) applied to computer vision. As shown, the system 700 canreceive information via an input layer, analyze information via hiddenlayers and output information via an output layer. The informationreceived at the input layer can be images such as facial images that arecomposed of pixels, which may be in a color space (e.g., RGB, grayscale,etc.).

The system 700 may learn (e.g., be trained) in one or more manners.Learning may be deep learning. As an example, learning can includeunsupervised learning, reinforcement learning, supervised learning,semi-supervised learning, etc.

Deep learning can be applied to tasks where a basic unit, a singlepixel, a single frequency, or a single word/character may have arelatively small amount of meaning in and of itself but where acombination of units has a relatively larger amount of meaning. As anexample, a combination of units may be assessed as to individual valuesof the units, which may be collectively useful. As an example, a methodcan include deep learning of useful combinations of values without humanintervention. For example, consider deep learning's ability to learnfeatures from data of a dataset of handwritten digits. In such anexample, when presented with tens of thousands of handwritten digits, adeep neural network can learn that it is useful to look for loops andlines when trying to classify the digits.

Deep learning can be implemented using one or more techniques ortechnologies, such as, for example, an optimizer, stochastic gradientdescent, unsupervised data pre-training of models to automate featureextraction, transfer functions, large data set(s) size, multipleprocessors (e.g., GPUs and/or CPUs) to accommodate considerablecomputational costs incurred by deep neural network models combined withlarge datasets, etc.

As an example, each successive layer in a neural network can utilizefeatures from a previous layer to learn more complex features. Consideran example, with reference to the system 700 of FIG. 7 , an approachwhere, at the lowest level, the neural network fixates on patterns oflocal contrast. A next layer can then use those patterns of localcontrast to fixate on data that resemble eyes, noses, and mouths asfacial features. Another subsequent layer can (e.g., a top layer) canthen apply those facial features to face templates. In such an example,a deep neural network is capable of composing features of increasingcomplexity in each of its successive layers.

Thus, the system 700 can perform automated learning of datarepresentations and features. Such an application of deep neuralnetworks may include models that can learn useful hierarchicalrepresentations of images, audio and written language. For example,consider these learned feature hierarchies in these domains can beconstrued as:

Image recognition: Pixel→edge→texton→motif→part→object

Text: Character→word→word group→clause→sentence

Speech: Sample→spectral band→sound→ . . . →phone→phoneme→word

Another example of DNN technology exists in AlphaGo (Silver, et. al,Mastering the Game of Go with Deep Neural Networks and Tree Search.Nature, Vol. 529, 28 Jan. 2016, p. 484-489 and appendixes and extendeddata, which are incorporated by reference herein) as applied to the game“Go”, an abstract strategy board game for two players, in which the aimis to surround more territory than the opponent. Go has relativelysimple rules yet is very complex, even more so than chess, and possessesa large number of possibilities. Compared to chess, Go has both a largerboard with more scope for play and longer games, and, on average, manymore alternatives to consider per move. In Go, the playing pieces arecalled stones. One player uses the white stones and the other, black.The players take turns placing the stones on the vacant intersections(named “points”) of a board with a 19×19 grid of lines.

In AlphaGo, DNN technology is trained to learn heuristics that guidesearch more efficiently in a massive search space. AlphaGo utilizes twokinds of DNNs: a value network to evaluate board positions and a policynetwork used to evaluate moves, and they are trained by a mix ofsupervised learning through studying examples provided by human andself-playing in the style of reinforcement learning. These pre-trainedDNNs allow the heuristic search algorithm (through Monte Carlo sampling)to identify promising moves without looking ahead thousands of steps andeffectively beat world-class human players despite a search spaceincluding an astronomical number of states.

As mentioned, planning, such as well planning or more generallyoperations planning, may be performed in an automated and/orsemi-automated manner. Theoretically, even without numeric conditionsand effects, automated planning can be in the complexity class ofPSPACE-complete, and undecidable when numeric reasoning is included.Planning can be at least as difficult as Go (PSPACE-hard without Ko).

As an example, plan generation can involve traversing a huge searchtree, and finding a path from an initial state to a goal state, whichcan be computationally intractable.

A planning task or workflow may be expressed in a Planning DomainDefinition Language (PDDL) that may be a PDDL that attempts tostandardize Artificial Intelligence (AI) planning languages. A PDDLplanner may use a generic heuristic based on a relaxed planning graph(RPG) approach to evaluate each state visited, computing heuristicvalues that indicate which state is closer to a goal than others, andexpanding from that state. Such a strategy, the so-called enforced hillclimbing process, can be supplemented by breadth-first search onplateaus. The RPG heuristic, although it can operate with variousplanning domains, can underestimate the distance to the goal, which canlead to poor guidance.

Learning planning heuristics using DNN can provide for generation ofmore adaptive and robust heuristics that may address more planningdomain instances and problem instances.

As an example, a system can utilize DNN techniques to learn planningheuristics and may include the following two parts: a supervisedlearning phase and a deep reinforcement learning phase.

As to a supervised learning phase, a DNN can be trained by planninginstances generated by a planner for a number of planning domains andproblems. By identifying structural representations of state that echospatial correlations, the deep convolutional network might involve aninput layer, convolutional layer, RELU layer, pooling layer and a fullyconnected layer. The training data can be generated following the methodof (Garret, et. al, Learning to Rank for Synthesizing PlanningHeuristics, International Joint Conference on Artificial Intelligence(IJCAI) 2016 (https://arxiv.org/abs/1608.01302), which is incorporatedby reference herein). As an example, through a supervised learningphase, a learner can at least learn the results slightly better thancurrent RPG-based heuristics (e.g., which may correspond to the valuenetwork in the technique of AlphaGo).

As to a deep reinforcement learning phase, after obtaining a preliminaryversion of a value network, a learner can evaluate applicable actionsagainst this network (e.g., as well as the domain-independent heuristic)and thus train a policy network following the approach of deepreinforcement learning. In such an approach, a policy network can belearned (e.g., which action is best in which state) which can eventuallyestablish a mapping between states and actions (a policy, in theterminology of reinforcement learning).

As to planning, a planner can search as follows. At each state, thevalue network is used to decide the most promising state to expand, andthe policy network is used to decide the most promising applicableaction to apply to expand that state.

As an example, a planning system can exploit deep learning in the realmof symbolic artificial intelligence and can exploit the influence ofdeep learning in the area of heuristic search. As an example, a planningsystem or planner can be implemented with highly adaptive planningheuristics for a wide range of tasks and/or with robust and efficientplanning algorithms that address large and real tasks.

As an example, a plan can construct sequences of actions which, whenapplied to an initial state, produce a goal state after some time haspassed. In such an example, a planner can search a massivelycombinatorial space of actions to find these sequences with powerfulsearch guidance. As an example, CNN-based Deep Learning techniques maybe implemented to learn search control for an automated planner.

FIG. 8 shows an example of a system 800 for machine classification andlearning. As shown, the system includes convolution and pooling layers,fully connected layers and Nx binary classifications as outputs. In theexample of FIG. 8 , input is a raw pixel image where convolution andnonlinearity techniques are implemented for finding spatially correlatedfeatures in the input image, for example, via maximum pooling.Convolutional networks can include local or global pooling that combineoutputs into an input or inputs (e.g., for another layer or layers).Maximum pooling (or “max pooling”) may use the maximum value from eachof a cluster of neurons at the prior layer while average pooling (or“ave pooling”) may use the average value from each of a cluster ofneurons at the prior layer.

As shown, the system 800 can compute classification probabilities for avector via fully connected layers such that classifications can be made,which, in the example of FIG. 8 , pertain to image content (e.g., bird,sunset, dog, cat, etc.). As indicated, subsequent convolution andpooling layers can find increasingly abstract features (e.g., patches ofcolor, edges, eyes, heads, etc.) within image data. Referring again tothe system 700 of FIG. 7 , some examples of features associated withhidden layers are shown, for example, the middle set of images maypertain to various types of curved or circular features (e.g., a pupilin an eye, an ear fold, a nostril opening, etc.).

FIG. 9 shows another example of a system 900 that pertains to the gameGo. The system 900 includes a policy network portion and a value networkportion. As shown, input includes 30 million positions (e.g., featuredata) and output includes a scalar estimated value of a given position.

In FIG. 9 , various types of hardware are referenced, including 50 GPUsand 250 CPUs. One or more types of circuitry may be utilized in acomputational framework that performs a process or processes as in FIG.9 . Time periods are also shown in FIG. 9 , including a 4 week period ofsupervised learning with stochastic gradient ascent, a 1 week periodwith reinforcement learning for policy gradient ascent plus bonus toencourage exploration (e.g., to avoid overtraining to a fixed set ofpossibilities), and a 1 week period with reinforcement learning forpolicy directed search. As shown, the value network learning pertains toboard position evaluation while the policy network learning pertains tomove selection. In FIG. 9 , an equation is shown that can allow forimplementation of a level of exploration.

As an example, a method can include converting plan inputs intopixel-grids that can be interpreted as images. In such an example, thepixel-grids can be given as input to an existing CNN-based imageinterpretation processes. Such an approach can allow an automated systemto “recognize” a state of a planned system or systems (e.g., a wellplan, a field plan for a plurality of wells, etc.).

As an example, a method can include identifying a “best” action toselect next. For example, where a well operation is underway accordingto a well plan, which can be an active digital well plan represented asan image (e.g., a pixel image), the well plan may be analyzed todetermine a current state. Given the current state, a system can thenidentify one or more modifications to the well plan for action(s) to betaken immediately and/or after a period of time or periods of time.

As an example, a computational framework may be utilized duringexecution of one or more plans as to field operations (e.g., rigoperations, etc.). In such an example, the computational framework mayoutput information that can include one or more of alarms,recommendations, control signals, etc. As an example, a computationalframework may provide for determining a current state and may providefor determining a next state where the next state can be a desirablestate that can be achieved via one or more actions. In such an example,the computational framework may output one or more signals that can bedirectly and/or indirectly utilized to achieve the desired next state.

FIG. 10 shows an example of a policy network image stack 1010 and avalue network image stack 1020 where each of the stacks has associatedhidden layers of a DNN. In such an example, the images can representstate information for one or more well plans. As mentioned with respectto the example of FIG. 9 , a policy network can provide for moveselection (e.g., action selection) and a value network can provide forstate evaluation (e.g., value of a state).

As to a neural network architecture, for the aforementioned AlphaGo, theinput to the policy network is a 19×19×48 image stack including 48feature planes. In such a system, the first hidden layer zero pads theinput into a 23×23 image, then convolves k filters of kernel size 5×5with stride 1 with the input image and applies a rectifier nonlinearity.Each of the subsequent hidden layers 2 to 12 zero pads the respectiveprevious hidden layer into a 21×21 image, then convolves k filters ofkernel size 3×3 with stride 1, again followed by a rectifiernonlinearity. The final layer convolves 1 filter of kernel size 1×1 withstride 1, with a different bias for each position, and applies a softmaxfunction. The match version of AlphaGo used k=192 filters. In theAlphaGo system, input to the value network is also a 19×19×48 imagestack, with an additional binary feature plane describing the currentcolour to play. Hidden layers 2 to 11 are as those in the policynetwork, hidden layer 12 is an additional convolution layer, hiddenlayer 13 convolves 1 filter of kernel size 1×1 with stride 1, and hiddenlayer 14 is a fully connected linear layer with 256 rectifier units. Theoutput layer of the AlphaGo system is a fully connected linear layerwith a single tan h unit. Further information is provided in Silver etal., Mastering the Game of Go with Deep Neural Networks and Tree Search.Nature, Vol. 529, 28 Jan. 2016, p. 484, which is incorporated byreference herein.

FIG. 11 shows a diagram of an example of a system 1100 for image-basedrepresentation of input plans. In such an example, rows can be timepoints in a plan (e.g., 0 up to plan horizon), columns can be statevariables and cells can be state variable values at time points.

In the system 1100, as time passes, values of the state variables changeand can be seen to ripple across the image. Correlation between statevariables can be observed at intermediate states of the system (thesecorrelations can be spatial, as represented by the block 1110).

In the system 1110, an image is not an image classified for visualunderstanding by a human or other animal that views the image, rather,the image is a construct of information that represents an operationalstate of equipment as may be an operational state of equipment utilizedfor drilling a well, completing a well, cementing a well, etc.

FIGS. 12 and 13 show examples of images 1200 and 1300, respectively. Theimage 1200 corresponds to a plan associated with the Maestro block 402of the system 400 of FIG. 4 and the image 1300 corresponds to a muddomain plan (e.g., as associated with drilling fluid).

In FIGS. 12 and 13 , the images are shown in grayscale; noting that theunderlying data are color images. As such, the images of FIGS. 12 and 13have color equivalents. For example, the images 1200 and 1300 can becolor coded as follows: each column of pixels in the image represents astate, and each pixel in the column is color coding a state variable inthe state; the state variables in a state (column) are listed accordingto their most recently added time in a plan, for example, assuming thatthere are two states (a,b) and (a,b,c), the first column includes twopixels in the order of (a,b), and the second column includes threepixels in the order of (c,a,b), meaning the more recently added factsare appended to the beginning of a column.

From the two images 1200 and 1300, different patterns exist that arelinked to particular nature of their corresponding domains. Suchrepresentations of states can be feed into a DNN learner.

FIG. 14 shows an example of a method 1400 that includes a representationblock 1410 for representing information for one or more plans as pixelsand/or voxels, a train block 1420 for training a deep neural network(DNN) based at least in part on the represented information, animplementation block 1430 for implementing the trained DNN, and anoutput block 1440 for outputting one or more types of information (e.g.,a generated plan, control signal(s), etc.).

As an example, the method 1400 can include, per the representation block1410, representing oilfield operational plan information as pixels wherethe pixels include pixels that correspond to a plurality of differentstate variables associated with oilfield operations; per the train block1420, training a deep neural network based at least in part on thepixels to generate a trained deep neural network; per the implementationblock 1430, implementing the trained deep neural network duringgeneration of an oilfield operational plan; and, per the output block1440, outputting the oilfield operational plan as a digital plan thatspecifies at least one control action for oilfield equipment.

FIG. 14 also shows various computer-readable media (CRM) blocks 1411,1421, 1431 and 1441 as associated with the blocks 1410, 1420, 1430 and1440. Such blocks can include instructions that are executable by one ormore processors, which can be one or more processors of a computationalframework, a system, a computer, etc. A computer-readable medium can bea computer-readable storage medium that is not a signal, not a carrierwave and that is non-transitory. For example, a computer-readable mediumcan be a physical memory component that can store information in adigital format.

In the example of FIG. 14 , a system 1490 includes one or moreinformation storage devices 1491, one or more computers 1492, one ormore networks 1495 and instructions 1496. As to the one or morecomputers 1492, each computer may include one or more processors (e.g.,or processing cores) 1493 and memory 1494 for storing the instructions1496, for example, executable by at least one of the one or moreprocessors. As an example, a computer may include one or more networkinterfaces (e.g., wired or wireless), one or more graphics cards, adisplay interface (e.g., wired or wireless), etc.

As an example, a trained DNN can be considered to be a heuristic toolthat can be implemented during generation of a plan, which can includeduring modification of a plan. As an example, a modification of a plancan include extending the plan in time and/or as to operations, etc. Forexample, where a plan is a well plan, the plan may be extended forproduction time or, for example, as to one or more completionsoperations that may not have been part of the plan (e.g., a priorversion of the plan).

As an example, a heuristic tool can include one or more heuristicfunctions (e.g., one or more heuristics), which may rank one or morealternatives with respect to one or more search algorithms, for example,at individual branching steps based on available information to provideas output information to help decide which branch to follow.

As an example, a learned heuristic can play a role in directing searchfor a plan during plan generation. Such an approach can be utilized todetermine an operation or operations, for example, in a sequence orsequences, which may be temporal or otherwise ordered (e.g., dependingon flow rate, depending on drilling, depending on a completionoperation, etc.).

As an example, a trained DNN can be utilized for directing search duringplan generation. As an example, a planner can access a trained DNNduring plan generation where the trained DNN can facilitate decisionmaking, which can be search result-based decision making.

As an example, a trained DNN can provide an informative heuristic toguide the search of a planner during plan generation. For example, atrained DNN can be implemented as a tool that facilitates choosingbetween states during search. As an example, a trained DNN can help toguide one or more searches for a plan during plan generation.

As an example, a trained DNN may be implemented with a planner,additionally or alternatively to a moderately informative heuristic(e.g., one that may not be learned but rather one that may be based on astandard relaxation of constraints). In such an example, a trained DNNmay be available as an option to replace a moderately informativeheuristic where the trained DNN is more informative. As mentioned such aDNN may be trained via use of plan information, which may be in the formof an image composed of pixels. In such an example, the image caninclude spatial information such that spatial correlations can part ofinput. A planner may access a trained DNN to leverage an approach thatincludes exploiting spatial correlations in input. A method can includerepresenting plan information as pixels (e.g., training information);and training a deep neural network based at least in part on the pixelsto generate a trained deep neural network. In such an example, themethod can utilize the trained deep neural network as a heuristic toolas part of a planner, for example, to facilitate searching and decisionmaking. A planner can exploit the power of one or more DNNs for thepurpose of planning.

As an example, a DNN can be used to modify a plan during execution ofthe plan as information is fed to the system from field operations (orother sources). Such an approach may also allow for on-going training ofthe DNN. In such an example, a method can include re-planning, which mayre-invoke one or more heuristics, which may include relearning at thatpoint. As an example, a DNN may be implemented in a manner that isoperatively coupled to systems control, whether for planning or foron-going operations. As an example, a DNN may be implemented at aplanning level and/or at a systems control level. In such an example,the DNN may be a DNN trained based on information cast in the form of animage, for example, an image made of pixels. A pixel image may be of asize and shape that may deviate from rectangle or square. A pixel imagemay be of a size and a shape that corresponds to information that is tobe input, which may be training information to train a DNN. Where a DNNis to be implemented in a planner during plan generation, the DNN can bea DNN trained via one or more pixel images (e.g., arrangement of pixelsthat represent plan information).

As an example, a pixel set can be defined by a grid and, for example, bya “space” or “spaces” such as one or more of a black and white space, agrayscale space and/or a color space. Such spaces may be coded spaceswhere a pixel can represent a state variable in a state via spacecoding. A shape and/or a size of a pixel image may depend on the natureof a domain or domains.

As an example, a method can include representing plan information aspixels; training a deep neural network based at least in part on thepixels to generate a trained deep neural network; and implementing thetrained deep neural network during generation of a plan. In such anexample, the deep neural network can include a policy network portionand a value network portion.

As an example, a plan can be or include a well plan. As an example, planinformation can include equipment information. As an example, planinformation can include well trajectory information.

As an example, pixels can be arranged as a two-dimensional array ofvalues. As an example, a deep neural network can be or include an imageanalysis deep neural network.

As an example, a plan can include time as a dimension. As an example, ata given time, a plan may be represented as pixels, which may be pixelsof a two-dimensional image. For example, consider pixels along onedimension in the image that represent a state with each pixel colorcoding a state variable in the state and, for example, pixels in anotherdimension in the image that represent state variables in a state (e.g.,where each pixel is listed according to its most recently added time inthe plan).

As an example, a method can include implementing a trained deep neuralnetwork by accessing the trained deep neural network as a heuristic toolthat facilitates search of a planner during generation of a plan by theplanner.

As an example, a method can include representing plan information aspixels; training a deep neural network based at least in part on thepixels to generate a trained deep neural network; implementing thetrained deep neural network during generation of a plan; and outputtingthe generated plan. Such a plan can include one or more operations thatare set forth in the plan based at least in part on one or moreheuristics of the trained deep neural network. As an example, aplurality of deep neural networks may be trained and implemented duringplanning by a planner to generate one or more plans. In such an example,at least one of the deep neural networks can be trained via informationrepresented as pixels.

As an example, a system can include a processor; memory accessible bythe processor; processor-executable instructions stored in the memoryand executable to instruct the system to: represent plan information aspixels; train a deep neural network based at least in part on the pixelsto generate a trained deep neural network; and implement the traineddeep neural network during generation of a plan, which can includemodification of an existing plan.

As an example, one or more computer-readable storage media can includeprocessor-executable instructions to instruct a computing system to:represent plan information as pixels; train a deep neural network basedat least in part on the pixels to generate a trained deep neuralnetwork; and implement the trained deep neural network during generationof a plan, which may include modification of an existing plan.

As an example, a method can include representing oilfield operationalplan information as pixels where the pixels include pixels thatcorrespond to a plurality of different state variables associated withoilfield operations; training a deep neural network based at least inpart on the pixels to generate a trained deep neural network;implementing the trained deep neural network during generation of anoilfield operational plan; and outputting the oilfield operational planas a digital plan that specifies at least one control action foroilfield equipment. In such an example, the deep neural network caninclude a policy network portion and a value network portion.

As an example, an oilfield operational plan can be or can include a wellplan. As an example, oilfield operational plan information can be or caninclude equipment information. As an example, oilfield operational planinformation can include well trajectory information.

As an example, pixels can be a two-dimensional array of values. In suchan example, a single pixel may be a single value, a duple, a tuple, etc.As an example, a single pixel may be multivalued according to a schemesuch as a color scheme (e.g., RGB, YUV, etc.). As an example, a singlepixel may include an array index or indexes (e.g., (101, 85) in a128×128 array).

As an example, a deep neural network can be or can include an imageanalysis deep neural network. As an example, a deep neural network canbe or can include an image analysis convolution neural network.

As an example, an oilfield operational plan can include time as adimension. As an example, an image can include time as a dimension. Forexample, consider states as columns and time along a row dimension wherea column represents a state at a particular time of a plurality oftimes.

As an example, pixels can be pixels of a two-dimensional image. In suchan example, pixels along one dimension in the image can represent astate (e.g., an operational state of field equipment with respect to ageologic environment, a wellbore, a trajectory, drilling fluid, etc.).As an example, in a two-dimensional image of pixels, each pixel alongone dimension in the image can be coded according to a grayscale schemeand/or coded according to a color scheme. For example, where a line ofpixels represents a state (e.g., along a dimension of thetwo-dimensional image of pixels), the individual pixels can be coded toindicate a value or values of a variable, which can be a state variable.

As an example, a method can include implementing a trained deep neuralnetwork at least in part by accessing the trained deep neural network asa heuristic tool that facilitates search of a planner during generationof an oilfield operational plan by the planner. For example, a plannercan execute via a computational framework where a search may beinitiated by the planner where the search may include a query (e.g., oneor more search terms, etc.). In such an example, the planner may accessa trained deep neural network to perform one or more searches accordingto the query. In such an example, the trained deep neural network canreturn one or more search results to the planner. As an example, aplanner may plan a state as an operational state to be achieved as partof a drilling operation for a well. In such an example, the planner mayact to plan a subsequent state and formulate a query based on one ormore planned states to instruct a trained deep neural network todetermine one or more candidate subsequent states that can possibleserve as the subsequent state. In such an example, an analogy to thegame “Go” may be “what my a next move?” where, in the context of theplan of the planner, the question may be “what is the next state for theplan?” where the next state may be selected from a ranking of states ora single state returned by a computational framework that implements thetrained deep neural network.

As an example, a method can include receiving a digital plan by acomputational framework. In such an example, the method can include,based at least in part on the digital plan and via the computationalframework, rendering a graphical user interface to a display where thegraphical user interface specifies at least one control action foroilfield equipment. Such a control action may be, for example, adrilling control action, a drilling fluid control action, a sensorcontrol action, a fracturing control action, a pull-out-of-hole (POOH)control action, etc.

As an example, a system can include a processor; memory accessible bythe processor; processor-executable instructions stored in the memoryand executable to instruct the system to: represent oilfield planinformation as pixels where the pixels include pixels that correspond toa plurality of different state variables associated with oilfieldoperations; train a deep neural network based at least in part on thepixels to generate a trained deep neural network; and implement thetrained deep neural network during generation of an oilfield operationalplan that specifies at least one control action for oilfield equipment.In such an example, pixels along one dimension in the image canrepresent a state.

As an example, one or more computer-readable storage media can includeprocessor-executable instructions to instruct a computing system to:represent oilfield plan information as pixels where the pixels includepixels that correspond to a plurality of different state variablesassociated with oilfield operations; train a deep neural network basedat least in part on the pixels to generate a trained deep neuralnetwork; and implement the trained deep neural network during generationof an oilfield operational plan that specifies at least one controlaction for oilfield equipment. In such an example, pixels along onedimension in the image can represent a state.

As an example, a method may be implemented in part usingcomputer-readable media (CRM), for example, as a module, a block, etc.that include information such as instructions suitable for execution byone or more processors (or processor cores) to instruct a computingdevice or system to perform one or more actions. As an example, a singlemedium may be configured with instructions to allow for, at least inpart, performance of various actions of a method. As an example, acomputer-readable medium (CRM) may be a computer-readable storage medium(e.g., a non-transitory medium) that is not a carrier wave.

According to an embodiment, one or more computer-readable media mayinclude computer-executable instructions to instruct a computing systemto output information for controlling a process. For example, suchinstructions may provide for output to sensing process, an injectionprocess, drilling process, an extraction process, an extrusion process,a pumping process, a heating process, etc.

In some embodiments, a method or methods may be executed by a computingsystem. FIG. 15 shows an example of a system 1500 that can include oneor more computing systems 1501-1, 1501-2, 1501-3 and 1501-4, which maybe operatively coupled via one or more networks 1509, which may includewired and/or wireless networks.

As an example, a system can include an individual computer system or anarrangement of distributed computer systems. In the example of FIG. 15 ,the computer system 1501-1 can include one or more modules 1502, whichmay be or include processor-executable instructions, for example,executable to perform various tasks (e.g., receiving information,requesting information, processing information, simulation, outputtinginformation, etc.).

As an example, a module may be executed independently, or incoordination with, one or more processors 1504, which is (or are)operatively coupled to one or more storage media 1506 (e.g., via wire,wirelessly, etc.). As an example, one or more of the one or moreprocessors 1504 can be operatively coupled to at least one of one ormore network interface 1507. In such an example, the computer system1501-1 can transmit and/or receive information, for example, via the oneor more networks 1509 (e.g., consider one or more of the Internet, aprivate network, a cellular network, a satellite network, etc.).

As an example, the computer system 1501-1 may receive from and/ortransmit information to one or more other devices, which may be orinclude, for example, one or more of the computer systems 1501-2, etc. Adevice may be located in a physical location that differs from that ofthe computer system 1501-1. As an example, a location may be, forexample, a processing facility location, a data center location (e.g.,server farm, etc.), a rig location, a wellsite location, a downholelocation, etc.

As an example, a processor may be or include a microprocessor,microcontroller, processor module or subsystem, programmable integratedcircuit, programmable gate array, or another control or computingdevice.

As an example, the storage media 1506 may be implemented as one or morecomputer-readable or machine-readable storage media. As an example,storage may be distributed within and/or across multiple internal and/orexternal enclosures of a computing system and/or additional computingsystems.

As an example, a storage medium or storage media may include one or moredifferent forms of memory including semiconductor memory devices such asdynamic or static random access memories (DRAMs or SRAMs), erasable andprogrammable read-only memories (EPROMs), electrically erasable andprogrammable read-only memories (EEPROMs) and flash memories, magneticdisks such as fixed, floppy and removable disks, other magnetic mediaincluding tape, optical media such as compact disks (CDs) or digitalvideo disks (DVDs), BLUERAY® disks, or other types of optical storage,or other types of storage devices.

As an example, a storage medium or media may be located in a machinerunning machine-readable instructions, or located at a remote site fromwhich machine-readable instructions may be downloaded over a network forexecution.

As an example, various components of a system such as, for example, acomputer system, may be implemented in hardware, software, or acombination of both hardware and software (e.g., including firmware),including one or more signal processing and/or application specificintegrated circuits.

As an example, a system may include a processing apparatus that may beor include a general purpose processors or application specific chips(e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriatedevices.

FIG. 16 shows components of a computing system 1600 and a networkedsystem 1610. The system 1600 includes one or more processors 1602,memory and/or storage components 1604, one or more input and/or outputdevices 1606 and a bus 1608. According to an embodiment, instructionsmay be stored in one or more computer-readable media (e.g.,memory/storage components 1604). Such instructions may be read by one ormore processors (e.g., the processor(s) 1602) via a communication bus(e.g., the bus 1608), which may be wired or wireless. The one or moreprocessors may execute such instructions to implement (wholly or inpart) one or more attributes (e.g., as part of a method). A user mayview output from and interact with a process via an I/O device (e.g.,the device 1606). According to an embodiment, a computer-readable mediummay be a storage component such as a physical memory storage device, forexample, a chip, a chip on a package, a memory card, etc.

According to an embodiment, components may be distributed, such as inthe network system 1610. The network system 1610 includes components1622-1, 1622-2, 1622-3, . . . 1622-N. For example, the components 1622-1may include the processor(s) 1602 while the component(s) 1622-3 mayinclude memory accessible by the processor(s) 1602. Further, thecomponent(s) 1622-2 may include an I/O device for display and optionallyinteraction with a method. The network may be or include the Internet,an intranet, a cellular network, a satellite network, etc.

As an example, a device may be a mobile device that includes one or morenetwork interfaces for communication of information. For example, amobile device may include a wireless network interface (e.g., operablevia IEEE 802.11, ETSI GSM, BLUETOOTH®, satellite, etc.). As an example,a mobile device may include components such as a main processor, memory,a display, display graphics circuitry (e.g., optionally including touchand gesture circuitry), a SIM slot, audio/video circuitry, motionprocessing circuitry (e.g., accelerometer, gyroscope), wireless LANcircuitry, smart card circuitry, transmitter circuitry, GPS circuitry,and a battery. As an example, a mobile device may be configured as acell phone, a tablet, etc. As an example, a method may be implemented(e.g., wholly or in part) using a mobile device. As an example, a systemmay include one or more mobile devices.

As an example, a system may be a distributed environment, for example, aso-called “cloud” environment where various devices, components, etc.interact for purposes of data storage, communications, computing, etc.As an example, a device or a system may include one or more componentsfor communication of information via one or more of the Internet (e.g.,where communication occurs via one or more Internet protocols), acellular network, a satellite network, etc. As an example, a method maybe implemented in a distributed environment (e.g., wholly or in part asa cloud-based service).

As an example, information may be input from a display (e.g., consider atouchscreen), output to a display or both. As an example, informationmay be output to a projector, a laser device, a printer, etc. such thatthe information may be viewed. As an example, information may be outputstereographically or holographically. As to a printer, consider a 2D ora 3D printer. As an example, a 3D printer may include one or moresubstances that can be output to construct a 3D object. For example,data may be provided to a 3D printer to construct a 3D representation ofa subterranean formation. As an example, layers may be constructed in 3D(e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example,holes, fractures, etc., may be constructed in 3D (e.g., as positivestructures, as negative structures, etc.).

Although only a few examples have been described in detail above, thoseskilled in the art will readily appreciate that many modifications arepossible in the examples. Accordingly, all such modifications areintended to be included within the scope of this disclosure as definedin the following claims. In the claims, means-plus-function clauses areintended to cover the structures described herein as performing therecited function and not only structural equivalents, but alsoequivalent structures. Thus, although a nail and a screw may not bestructural equivalents in that a nail employs a cylindrical surface tosecure wooden parts together, whereas a screw employs a helical surface,in the environment of fastening wooden parts, a nail and a screw may beequivalent structures. It is the express intention of the applicant notto invoke 35 U.S.C. § 112, paragraph 6 for any limitations of any of theclaims herein, except for those in which the claim expressly uses thewords “means for” together with an associated function.

What is claimed is:
 1. A method comprising: representing oilfieldoperational plan information as pixels of a multi-dimensional image,wherein the pixels comprise pixels that correspond to a plurality ofdifferent state variables associated with oilfield operations, wherein aline of the pixels along a first image dimension represents instances ofone or more of the state variables that collectively define an oilfieldoperational state, and wherein multiple lines of the pixels along asecond image dimension represent changes in oilfield operational stateswith respect to time; training a deep neural network based at least inpart on the pixels to generate a trained deep neural network, whereinthe deep neural network comprises an image analysis deep neural network;implementing the trained deep neural network during generation of anoilfield operational plan; and outputting the oilfield operational planas a digital plan that specifies at least one control action foroilfield equipment.
 2. The method of claim 1 wherein the deep neuralnetwork comprises a policy network portion and a value network portion.3. The method of claim 1 wherein the oilfield operational plan comprisesa well plan.
 4. The method of claim 1 wherein the oilfield operationalplan information comprises equipment information.
 5. The method of claim1 wherein the oilfield operational plan information comprises welltrajectory information.
 6. The method of claim 1 wherein the pixelscomprise a two-dimensional array of values.
 7. The method of claim 1wherein the deep neural network comprises an image analysis convolutionneural network.
 8. The method of claim 1 wherein the oilfieldoperational plan comprises time as a dimension.
 9. The method of claim 1wherein the pixels comprise pixels of a two-dimensional image.
 10. Themethod of claim 1 wherein each of the pixels along the first imagedimension is coded according to a grayscale scheme.
 11. The method ofclaim 1 wherein each of the pixels along the first image dimension iscoded according to a color scheme.
 12. The method of claim 1 whereinimplementing the trained deep neural network comprises accessing thetrained deep neural network as a heuristic tool that facilitates searchof a planner during generation of the oilfield operational plan by theplanner.
 13. The method of claim 1 comprising receiving the digital planby a computational framework.
 14. The method of claim 13 comprising,based at least in part on the digital plan and via the computationalframework, rendering a graphical user interface to a display wherein thegraphical user interface specifies at least one control action foroilfield equipment.
 15. A system comprising: a processor; memoryaccessible by the processor; processor-executable instructions stored inthe memory and executable to instruct the system to: represent oilfieldplan information as pixels of a multi-dimensional image, wherein thepixels comprise pixels that correspond to a plurality of different statevariables associated with oilfield operations, wherein a line of thepixels along a first image dimension represents instances of one or moreof the state variables that collectively define an oilfield operationalstate, and wherein multiple lines of the pixels along a second imagedimension represent changes in oilfield operational states with respectto time; train a deep neural network based at least in part on thepixels to generate a trained deep neural network, wherein the deepneural network comprises an image analysis deep neural network; andimplement the trained deep neural network during generation of anoilfield operational plan that specifies at least one control action foroilfield equipment.
 16. The system of claim 15 wherein the pixelscomprise pixels of a two-dimensional image.
 17. One or morecomputer-readable storage media comprising processor-executableinstructions to instruct a computing system to: represent oilfield planinformation as pixels of a multi-dimensional image, wherein the pixelscomprise pixels that correspond to a plurality of different statevariables associated with oilfield operations, wherein a line of thepixels along a first image dimension represents instances of one or moreof the state variables that collectively define an oilfield operationalstate, and wherein multiple lines of the pixels along a second imagedimension represent changes in oilfield operational states with respectto time; train a deep neural network based at least in part on thepixels to generate a trained deep neural network, wherein the deepneural network comprises an image analysis deep neural network; andimplement the trained deep neural network during generation of anoilfield operational plan that specifies at least one control action foroilfield equipment.
 18. The one or more computer-readable storage mediaof claim 17 wherein the pixels comprise pixels of a two-dimensionalimage.